The present invention relates in general to electronic communication devices and, more particularly, to electronic communication devices incorporating speech recognition algorithms.
Markov models, Dynamic Time Warping (DTW) and neural-net microprocessors have been applied to machine recognition of speech. Markov models are based on a mathematical structure that forms the theoretical basis for a wide range of applications. When Markov models are applied to speech recognition, the models are referred to as the Hidden Markov Models (HMM) to include the case where the observation is a probabilistic function of the state. A state transition matrix based on specific observations provides a probability density distribution matrix. Thus, the Hidden Markov Models used in speech recognition are characterized by a process that provides evaluation of the probability or likelihood of a sequence of speech sounds.
Typically, a speech recognition system using HMM includes a feature analysis block that provides observation vectors used for training the HMMs that characterize various speech sounds. A unit-matching block provides the likelihood of a match of all sequences of speech recognition units to an unknown input speech sound. A lexical decoding block places constraints on the unit-matching block so that the paths investigated are those corresponding to sequences of speech sounds that are in a word dictionary. Syntactic and semantic analysis blocks further constrain the paths investigated to provide higher performance of the speech recognition system.
Speech recognition is becoming more prevalent, but new techniques are needed to make applications more reliable. The demand for higher speech recognition accuracy is relentless, requiring continuous improvements in the performance of speech recognition algorithms. Accordingly, it would be advantageous to have a method for developing a set of speech building blocks that improve a speech recognition system.